
Expected Error Reduction: This approach is interrelated to uncertainty sampling in such a way that uncertainty measures maximize the label uncertainty of the sample to be queried for the label while expected error reduction reduces the label uncertainty of the queried sample. Referring to the already discussed example of the binary classification problem, the expected error reduction approach would choose the samples with a probability far away from \(0.5\)  in order to reduce the error rate. Such techniques are also known as the greatest certainty models [1]}.

Expected Variance Reduction: Reducing the variance of the model is guaranteed to reduce future generalization error [2]}. Therefore, expected variance reduction techniques attempt to indirectly reduce the generalization error by minimizing the model variance. Such approaches query the instances that result in the lowest model variance. The Fisher information ratio is a well-known variance minimization framework.

